Preliminary Evaluation of Dental Cone-beam CT

Preliminary Evaluation of Dental Cone-beam CT Image from Reduced Projection Data by Constrained-TV-minimization Zheng Zhang, Xiao Han, Budi Kusnoto, E. Y. Sidky and Xiaochuan Pan

Abstract—Cone-beam computed tomography (CBCT) has gained increasing acceptance in general dentistry and orthodontics during the past decade. Nevertheless, dental CBCT delivers considerable radiation dose, which raises concern about the potential risk. One of the approaches to lower the radiation dose in CBCT data acquisition is to reduce the total number of projections. However, image quality may be degraded when current analytic-based algorithms are used for reconstructing images from sparse-view CBCT data. Recently, many optimization-based algorithms have been investigated for image reconstruction from data containing reduced projections. In this work, we apply the adaptive-steepest-descent (ASD)projection-onto-convex-set (POCS) algorithm to reconstructing images from full (300)- and sparse (151 and 76)-view dental CBCT data sets. The result shows that the ASD-POCS algorithm can reconstruct from 300-view and 151-view data images with quality comparable to, or improved over the clinical images. The ASD-POCS reconstruction from 76-view data has visibly degraded quality, but may still yield potential practical utility in certain clinical tasks.

I. I NTRODUCTION Cone-beam computed tomography (CBCT) has gained increasing acceptance in general dentistry and orthodontics during the past decade [1], [2]. Dental CBCT provides threedimensional images, which eliminate superimposition and distortion effects existing in two-dimensional X-ray imaging. The three-dimensional images enable clinicians to have more accurate anatomic information and more intuitive observation of structures of interest. Meanwhile, two-dimensional X-ray images, such as projection radiographs, can still be generated from the three-dimensional image. Although dental CBCT possesses many advantages, concerns about potential radiation risk exist because it generally delivers more radiation dose than conventional 2D X-ray imaging [3]. In particular, because children and adolescents are more sensitive to radiation [4], it is of great merit to lower the imaging dose in the CBCT scans for them. One way to lowering the imaging dose is to reduce the total number of projections while maintaining the exposure per projection the same. However, image reconstruction from sparse-view data often poses challenge to clinically used reconstruction algorithms, and results in inferior image quality, which may affect diagnosis or assessment. Z. Zhang, X. Han, E. Y. Sidky and X. Pan are with The University of Chicago. B. Kusnoto is with Departments of Orthodontics, the University of Illinois at Chicago.

Recently, a great body of studies has been carried out to develop optimization-based algorithms for exploiting image reconstruction from sparse-view data. One of such algorithms is adaptive-steepest-descent (ASD)-projection-ontoconvex-set (POCS) algorithm [5]–[9], which has demonstrated the potential to improve image quality and to reconstruct images with practical utilities in non-conventional conditions. In this work, we perform optimization-based image reconstruction by using the ASD-POCS algorithm from dental CBCT data. In particular, we focus on sparseview image reconstructions. II. M ATERIALS AND METHODS A. CBCT Imaging System In the work, we collect data with an i-CAT CBCT system (Imaging Sciences International, Hatfield, PA). In the i-CAT system, the distances from the source to the rotation axis and to the detector are 49.35 cm and 71.03 cm, respectively. The detector consists of a 480 × 384 array with an element size of 0.508 × 0.508 mm2 . The diameter of the field-of-view (FOV) is about 13 cm within the transverse plane. B. Data Acquisition A patient data set was collected at 300 views over 2π. The patient was scanned with tube voltage of 120 kV and tube current of 20 mAs. The measured data contain truncation because the scan FOV is insufficient to cover the entire object support. We refer to the 300-view data as the full data, from which we extracted sparse-view data sets at 151 and 76 views uniformly distributed over 2π. We then perform image reconstruction from the full- and sparse-view data sets. C. Optimization-Based Imaging Model In an optimization-based reconstruction, the model data g0 and image f are vectors with M pixels and N voxels, respectively, and a discrete-to-discrete (D-D) linear model links the two vectors [5]–[7], which can be written as: g0 = Hf.

(1)

H denotes the system matrix of size M × N. We employ a ray-driven projection model to calculate elements of H. The properties of H are affected by the data sampling, for example, the number of projection views.

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D. Optimization-Based Reconstruction In this work, we minimization program f∗ = argmin k f kTV

consider s.t.

the

constrained-TV-

f ≥ 0 and

D(f) ≤ ǫ, (2)

where D(f) =| Hf − g | denotes the Euclidean-data divergence, g are measured data, and ǫ is a pre-selected, positive parameter for accommodating inconsistencies between data and the model. We use the ASD-POCS algorithm [5]–[9] that has been developed previously to solve the optimization program in Eq. (2). The algorithm uses alternatingly the POCS to reduce the data divergence and the TV gradient descent to lower the image TV. In the study, we reconstructed images from the acquired full data and the extracted sparseview data sets by using the ASD-POCS algorithm. The reconstructed image array size is 536 × 536 × 440, with a voxel size of 0.3 × 0.3 × 0.3 mm3 . E. Image Evaluation In the work, we focus on evaluating sparse-view ASDPOCS reconstructions that if they can yield clinical utilities; while the full-view ASD-POCS result is shown as the upper bound for sparse-view reconstructions, and we want to investigate if ASD-POCS algorithm can improve the image quality in current dental CBCT imaging protocols. We obtain clinical images from the i-CAT CBCT system, which are reconstructed by use of the FDK algorithm [10]. These clinical images are used as the gold standard for evaluating ASD-POCS reconstructions. We carry out two evaluation studies: a visualization study for assessing the quality of ASD-POCS reconstructions, and an accuracy study [11] to evaluate the accuracy of those images when used as diagnostic cephalometric analysis and measurement (in orthodontics and oral surgery purposes). In the visualization study, we compare ASD-POCS reconstructions to the clinical images by using the cross-sectional CBCT images. Two-dimensional sets of lateral and posterioranterior cephalograms are generated from 3D CBCT images by using software Dolphin 3D (Dolphin Imaging & Management Solutions, Chatsworth, CA). The two-dimensional images are evaluated by observers recruited from UIC School of Dentistry, including orthodontic residents, orthodontic faculties, oral surgery faculties, oral surgery residents and oral and maxillofacial radiologists. In the study, observers blindly evaluate the two-dimensional images of the clinical images, the full- and sparse-view ASD-POCS reconstructions, respectively [12]. Observers assess each image if it yields diagnostic acceptance ("yes" is recorded as 1 and "no" as 0), and rank each image on a visual analog scale from 1 to 10 (1 is the poorest quality and 10 is the best quality). Statistical analyses, such as Cochran test, McNemar test, Friedman test, and Wilcoxon test, are conducted based on observers’ evaluation. In the accuracy study to determine the landmark locations, we evaluate two-dimensional images, such as lateral

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cephalograms and posterior-anterior cephalograms. We enroll orthodontists to label and trace 65 anatomical landmarks in these two-dimensional images. Inter and intra observer reliabilities are tested and found consistent. III. R ESULTS A. Cross-Sectional Images We first compare cross-sectional CBCT images between the clinical image and the ASD-POCS reconstructions from full- and sparse-view data sets. We display in Fig. 1 the clinical image within two transverse slices and one sagittal slice, and use white boxes to enclose structures of root canals in Fig. 1a, sinus in Fig. 1b, and aerated mastoid bone in Fig. 1c, respectively. Those structures are of great interest in many applications in general dentistry as well as orthodontics. For detailed comparison between the clinical image and ASD-POCS reconstructions, we display in Figs. 2-4 zoomed-in images within those ROIs. Reconstruction from 300-view data (full data) We first compare ROI images in the full-view ASD-POCS reconstruction to those in the clinical image, which are shown in Figs. 2-4. By comparing the root canals in Fig. 2, we observe that the full-view ASD-POCS reconstruction is comparable to the clinical image, with slightly enhanced contrast and improved sharpness. In Fig. 3, closer inspection of the sinus reveals that the full-view ASD-POCS reconstruction has better defined boundaries of the structures. In Fig. 4, rich details can be found within the aerated mastoid bone in the full-view ASD-POCS result, which appear to be somewhat obscured in the clinical image. Reconstruction from 151-view data We then compare the ROIs in the 151-view ASD-POCS result to those in the clinical image as well as in the full-view ASD-POCS reconstruction in Figs. 2-4. Observation can be made that the 151-view ASD-POCS reconstruction is comparable to the clinical image. We also notice that the 151-view ASDPOCS result shows better sharpness of the sinus structures than the clinical image in Fig. 3. In Fig. 4, there are still more details of the complex structures within the aerated mastoid bone in the 151-view ASD-POCS reconstruction than in the clinical image. Moreover, we notice that the reduction of view numbers from 300 to 151 appears to have a less noticeable impact on ASD-POCS reconstructions. Reconstruction from 76-view data Finally, we inspect the ROIs in the image reconstructed from 76-view data in Figs. 2-4 by use of the ASD-POCS algorithm. Comparing to the corresponding ROIs in the clinical image and in the fullview ASD-POCS reconstruction, the 76-view ASD-POCS result is visibly degraded due to substantial data reduction. However, in Figs. 2 and 3 we can still delineate the inner contour of the root canals and the sinus. By inspecting the fine structures within the aerated mastoid bone in Fig. 4, we observe that the 76-view ASD-POCS result shows comparable sharpness to the clinical image. Those results indicate that the 76-view ASD-POCS reconstruction may


(a)

(b)

(c)

Figure 1. Images of the patient within transverse slices (a, b) and within a sagittal slice (c). ROIs are enclosed by solid boxes, which show root canals (a), sinus (b), and aerated mastoid bone (c). Display window: [-1000, 1800] HU.

Figure 3. Zoomed-in view of ROI in (c) of Fig. 1. Top left: clinical image; Top right: ASD-POCS image reconstructed from 300-view data; Bottom left: ASD-POCS image reconstructed from 151-view data; Bottom right: ASD-POCS image reconstructed from 76-view data. Display window: [-1000, 1500] HU.

Figure 2. Zoomed-in view of ROI in (a) of Fig. 1. Top left: clinical image; Top right: ASD-POCS image reconstructed from 300-view data; Bottom left: ASD-POCS image reconstructed from 151-view data; Bottom right: ASD-POCS image reconstructed from 76-view data. Display window: [0, 1800] HU.

possess potential utility for certain clinical tasks that do not need very detailed information. B. Human Observer Study We carried out human observer study for qualitative assessment. Professionals participate the study to evaluate the lateral cephalograms and the posterior-anterior cephalograms. The results demonstrate that the full-view ASDPOCS result is more favorable relative to the clinic image. The 151-view ASD-POCS reconstruction still produces results comparable to the clinical images, which can be used for the routine diagnosis. When the projection number is pushed down to 76, although observers consider that the 76-view ASD-POCS reconstruction is a little inferior to

Figure 4. Zoomed-in view of ROI in (d) of Fig. 1. Top left: clinical image; Top right: ASD-POCS image reconstructed from 300-view data; Bottom left: ASD-POCS image reconstructed from 151-view data; Bottom right: ASD-POCS image reconstructed from 76-view data. Display window: [-800, 1300] HU.


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the clinical image when evaluating the posterior-anterior cephalograms, there are still few differences between them in the lateral cephalograms. In particular, observers consider the lateral cephalogram generated from the 76-view ASDPOCS reconstruction can be used for orthodontic diagnosis. C. Accuracy Study In the accuracy study, orthodontists were recruited to trace 65 cephalometric landmarks. Average error of the location of the 65 cephalometric landmarks was found to be (1.07 mm ± 0.68 mm) for the 2D images from 151view ASD-POCS reconstruction, which is comparable to the i-CAT CBCT gold standard (1.00 mm ± 0.50 mm) when used for cephalometric measurements and analyses. In some instances, even the 2D images from the 76-view ASD-POCS reconstruction still produced clinically acceptable accuracy (1.44 mm ± 0.86 mm). IV. D ISCUSSIONS In this work, we have investigated the application of the ASD-POCS algorithm to reconstructing images from fulland sparse-view patient data collected with an i-CAT CBCT system. We evaluated the ASD-POCS reconstructions from full data as well as sparse-view data sets containing 151 and 76 views, and compared them to the clinical image. We first carried out visualization for assessing the image quality by using the CBCT cross-sectional images; we then carried out human observer study for evaluating the diagnostic acceptance of the ASD-POCS reconstructions, based on lateral cephalograms and posterior-anterior cephalograms; finally, we conducted accuracy study to evaluate the accuracy of those images when used as diagnostic cephalometric analysis and measurement (in orthodontics and oral surgery purposes). Results show that ASD-POCS reconstruction from full data is comparable to, or better than the current clinical image. The 151-view ASD-POCS reconstruction is also comparable to the clinical image, and can be used for the routine diagnosis. The 76-view ASD-POCS reconstruction, although visibly degraded in the cross-sectional images, is comparable to the clinical image when using the lateral cephalogram. The work suggests that the ASD-POCS algorithm may be used for dental CBCT image reconstruction from data containing reduced projections, which lowers the imaging dose and yields images of practical utility.

R EFERENCES [1] J. Ludlow, L. Ludlow, and S. Brooks, “Dosimetry of two extraoral direct digital imaging devices: NewTom cone beam CT and Orthophos Plus DS panoramic unit,” Dentomaxillofacial Radio., vol. 32, pp. 229 – 234, 2003. [2] J. Ludlow, L. Ludlow, S. Brooks, and W. Howerton, “Dosimetry of 3 CBCT devices for oral and maxillofacial radiology: CB Mercuray, NewTom 3G and i-CAT,” Dentomaxillofacial Radio., vol. 35, pp. 219 – 226, 2005. [3] J. Roberts, N. Drage, J. Davies, and D. Thomas, “Effective dose from cone beam CT examinations in dentistry,” British journ. Radio., vol. 82, pp. 35 – 40, 2008. [4] T. Underhill, I. Chilvarquer, K. Kimura, R. Langlais, W. McDavid, J. Preece, and G. Barnwell, “Radiobiologic risk estimation from dental radiology. Part I. Absorbed doses to critical organs,” Oral surg., Oral med., Oral Path., Oral Radio., & Endoont., vol. 66, pp. 111 – 120, 1988. [5] E. Y. Sidky, K.-M. Kao, and X. Pan, “Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT,” J. XRay Sci. and Technol., vol. 14, pp. 119–139, 2006. [6] E. Y. Sidky and X. Pan, “Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,” Phys. Med. Biol., vol. 53, pp. 4777–4807, 2008. [7] X. Pan, E. Y. Sidky, and M. Vannier, “Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction?” Inverse Probl., vol. 25, p. 123009, 2009. [8] J. Bian, J. H. Siewerdsen, X. Han, E. Y. Sidky, J. L. Prince, C. A. Pelizzari, and X. Pan, “Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam CT ,” Phys. Med. Biol., vol. 55, pp. 6575–6599, 2010. [9] X. Han, J. Bian, D. R. Eaker, T. L. Kline, E. Y. Sidky, E. L. Ritman, and X. Pan, “Algorithm-enabled low-dose micro-CT imaging,” IEEE Trans. Med. Imag., vol. 30, pp. 606–620, 2011. [10] L. A. Feldkamp, L. C. Davis, and J. W. Kress, “Practical cone-beam algorithm,” J. Opt. Soc. Am. A, vol. 1, pp. 612–619, 1984. [11] A. Salem, “Feasibility study on the reduced-projection-algorithm for 2D cephalograms,” MS thesis, University of Illinois at Chicago, 2014. [12] P. Kaur, “Evaluation radiographic orthodontic records image quality derived from CBCT,” MS thesis, University of Illinois at Chicago, 2014.

V. ACKNOWLEDGMENTS We thank Abdelrahman Salem and Pardeep Kaur (The University of Illinois at Chicago) for carrying out the observer study and the accuracy study. This work was supported in part by NIH R01 Grant Nos. CA120540, CA158446, and EB000225. The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health.

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